#loading data 
load("house_elections.rda")
load("candidates.rda")
load("committees.rda")
load("contributions.rda")
library(tidyverse)
require(scales)
library(ggthemes)
#filtered candidates
candidates_filtered <- candidates %>% 
   select(-c(cand_st1, cand_st2, cand_city, cand_state,cand_zip))

#filtered contributions
contributions_filtered <- contributions %>% 
  select(cmte_id, name, city, state, zip_code, transaction_dt, transaction_amt, cand_id, other_id)

 #filter committees 
committees_filtered <- committees %>% 
  select(cmte_id, cmte_name, cmte_city, cmte_state, cmte_zip, cmte_party_affiliation, cand_id)
#joining the filtered tables
complete_table <- left_join(candidates_filtered, contributions_filtered, by = "cand_id")
#creating list of state names for function
c <- complete_table %>%
  select(cand_office_state, cand_election_yr, cand_office_state) %>%
  distinct(cand_office_state)

kimtest <- complete_table %>%
  filter(cand_election_yr == "2012", cand_office == "S") 
#Final list
state_names <- distinct(kimtest, cand_office_state) %>%
  .$cand_office_state
#lubridate
require(lubridate)
#changes int dates into actual dates
dates <- complete_table %>% 
  mutate(date = mdy(transaction_dt))
# sorting the complete table, only interested in Senate, 2012 races in the timeline of the election year
filtered_names <- dates %>%
  filter(cand_election_yr == "2012", cand_office == "S", date >= "2012-01-01")%>%  
  group_by(cand_name, transaction_dt) %>%
  mutate(total = sum(transaction_amt))
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blog post We used data from the Federal Election Commission